Consolidating prior temporally-matched frames in 3d-based video denoising

ABSTRACT

In one system embodiment, an overlapped block processing module configured to provide three-dimensional (3D) denoising of plural frames corresponding to a raw video sequence; and a frame alignment module configured to represent the raw video sequence with motion compensated frames corresponding to the raw video sequence, the motion compensated frames consisting of the plural frames and fewer in quantity than the quantity of frames of the raw video sequence, the plural frames based on prior temporally matched frames corresponding to the raw video sequence.

TECHNICAL FIELD

The present disclosure relates generally to video noise reduction.

BACKGROUND

Filtering of noise in video sequences is often performed to obtain asclose to a noise-free signal as possible. Spatial filtering requiresonly the current frame (i.e. picture) to be filtered and not surroundingframes in time. Spatial filters, when implemented without temporalfiltering, may suffer from blurring of edges and detail. For this reasonand the fact that video tends to be more redundant in time than space,temporal filtering is often employed for greater filtering capabilitywith less visual blurring. Since video contains both static scenes andobjects moving with time, temporal filters for video include motioncompensation from frame to frame for each part of the moving objects toprevent trailing artifacts of the filtering.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the disclosure can be better understood with referenceto the following drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a block diagram that illustrates an example environment inwhich video denoising (VDN) systems and methods can be implemented.

FIGS. 2A-2C are schematic diagrams that conceptually illustrateprocessing implemented by various example embodiments of VDN systems andmethods.

FIG. 3 is a block diagram that illustrates one example VDN systemembodiment comprising frame alignment and overlapped block processingmodules.

FIG. 4A is a block diagram that illustrates one example embodiment of aframe alignment module.

FIG. 4B is a block diagram that illustrates another example embodimentof a frame alignment module.

FIG. 5 is a block diagram that illustrates one example embodiment of aframe matching module of a frame alignment module.

FIGS. 6A-6D are block diagrams that illustrate a modifiedone-dimensional (1D) transform used in an overlapped block processingmodule, the 1D transform illustrated with progressively reducedcomplexity.

FIG. 7 is a schematic diagram that conceptually illustrates the use oftemporal modes in an overlapped block processing module.

FIG. 8 is a schematic diagram that illustrates an example mechanism forthresholding.

FIG. 9 is a flow diagram that illustrates an example method embodimentfor decoupled frame matching and overlapped block processing.

FIGS. 10A-10B are flow diagrams that illustrate example methodembodiments for frame matching.

FIG. 11 is a flow diagram that illustrates an example method embodimentfor frame matching and video denoising that includes various embodimentsof accumulation buffer usage.

FIGS. 12A-12B are flow diagrams that illustrate example methodembodiments for determining noise thresholding mechanisms.

FIG. 13 is a flow diagram that illustrates an example method embodimentfor adaptive thresholding in video denoising.

FIG. 14 is a flow diagram that illustrates an example method embodimentfor filtered and unfiltered-based motion estimation.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

In one system embodiment, an overlapped block processing moduleconfigured to provide three-dimensional (3D) denoising of plural framescorresponding to a raw video sequence; and a frame alignment moduleconfigured to represent the raw video sequence with motion compensatedframes corresponding to the raw video sequence, the motion compensatedframes consisting of the plural frames and fewer in quantity than thequantity of frames of the raw video sequence, the plural frames based onprior temporally matched frames corresponding to the raw video sequence.

Example Embodiments

Disclosed herein are various example embodiments of video denoising(VDN) systems and methods (collectively, referred to herein also as aVDN system or VDN systems) that comprise a frame alignment module and anoverlapped block processing module, the overlapped block processingmodule configured to denoise video in a three-dimensional (3D) transformdomain using motion compensated overlapped 3D transforms. In particular,certain embodiments of VDN systems motion compensate a set of framessurrounding a current frame, and denoise the frame using 3Dspatio-temporal transforms with thresholding of the 2D and/or 3Dtransform coefficients. One or more VDN system embodiments provideseveral advantages or distinctive features over brute force methods ofconventional systems, including significantly reduced computationalcomplexity that enable implementation in real-time silicon (e.g.,applicable to real-time applications, such as pre-processing of framesfor real-time broadcasting of encoded pictures of a video stream), suchas non-programmable or programmable hardware including fieldprogrammable gate arrays (FPGAs), and/or other such computing devices.Several additional distinctive features and/or advantages, explainedfurther below, include the decoupling of block matching and inverseblock matching from an overlapped block processing loop, reduction ofaccumulation buffers from 3D to 2D+n (where n is an integer number ofaccumulated frames less than the number of frames in a 3D buffer), andthe “collapsing” of frames (e.g., taking advantage of the fact thatneighboring frames have been previously frame matched to reduce theamount of frames entering the overlapped block processing loop whileobtaining the benefit of the information from the full scope of framesfrom which the reduction occurred for purposes of denoising). Suchfeatures and/or advantages enable substantially reduced complexity blockmatching. Further distinctive features include, among others, acustomized-temporal transform and temporal depth mode selection, alsoexplained further below.

These advantages and/or features, among others, are describedhereinafter in the context of an example subscriber television networkenvironment, with the understanding that other video environments mayalso benefit from certain embodiments of the VDN systems and methods andhence are contemplated to be within the scope of the disclosure. Itshould be understood by one having ordinary skill in the art that,though specifics for one or more embodiments are disclosed herein, suchspecifics as described are not necessarily part of every embodiment.

FIG. 1 is a block diagram of an example environment, a subscribertelevision network 100, in which certain embodiments of VDN systemsand/or methods may be implemented. The subscriber television network 100may include a plurality of individual networks, such as a wirelessnetwork and/or a wired network, including wide-area networks (WANs),local area networks (LANs), among others. The subscriber televisionnetwork 100 includes a headend 110 that receives (and/or generates)video content, audio content, and/or other content (e.g., data) sourcedat least in part from one or more service providers, processes and/orstores the content, and delivers the content over a communication medium116 to one or more client devices 118 through 120. The headend 110comprises an encoder 114 having video compression functionality, and apre-processor or VDN system 200 configured to receive a raw videosequence (e.g., uncompressed video frames or pictures), at least aportion of which (or the entirety) is corrupted by noise. Such noise maybe introduced via camera sensors, from previously encoded frames (e.g.,artifacts introduced by a prior encoding process from which the rawvideo was borne, among other sources). The VDN system 200 is configuredto denoise each picture or frame of the video sequence and provide thedenoised pictures or frames to the encoder 114, enabling, among otherbenefits, the encoder to encode fewer bits than if noisy frames wereinputted to the encoder. In some embodiments, at least a portion of theraw video sequence may bypass the VDN system 200 and be fed directlyinto the encoder 114.

Throughout the disclosure, the terms pictures and frames are usedinterchangeably. In some embodiments, the uncompressed video sequencesmay be received in digitized format, and in some embodiments,digitization may be performed in the VDN system 200. In someembodiments, the VDN system 200 may comprise a component that may bephysically and/or readily de-coupled from the encoder 114 (e.g., such asin the form of a plug-in-card that fits in a slot or receptacle of theencoder 114). In some embodiments, the VDN system 200 may be integratedin the encoder 114 (e.g., such as integrated in an applications specificintegrated circuit or ASIC). Although described herein as apre-processor to a headend component or device, in some embodiments, theVDN system 200 may be co-located with encoding logic at a client device,such as client device 118, or positioned elsewhere within a network,such as at a hub or gateway.

The headend 110 may also comprise other components, such as QAMmodulators, routers, bridges, Internet Service Provider (ISP) facilityservers, private servers, on-demand servers, multi-media messagingservers, program guide servers, gateways, multiplexers, and/ortransmitters, among other equipment, components, and/or deviceswell-known to those having ordinary skill in the art. Communication ofInternet Protocol (IP) packets between the client devices 118 through120 and the headend 110 may be implemented according to one or more of aplurality of different protocols, such as user datagram protocol(UDP)/IP, transmission control protocol (TCP)/IP, among others.

In one embodiment, the client devices 118 through 120 comprise set-topboxes coupled to, or integrated with, a display device (e.g.,television, computer monitor, etc.) or other communication devices andfurther coupled to the communication medium 116 (e.g., hybrid-fibercoaxial (HFC) medium, coaxial, optical, twisted pair, etc.) via a wiredconnection (e.g., via coax from a tap) or wireless connection (e.g.,satellite). In some embodiments, communication between the headend 110and the client devices 118 through 120 comprises bi-directionalcommunication over the same transmission medium 116 by which content isreceived from the headend 110, or via a separate connection (e.g.,telephone connection). In some embodiments, communication medium 116 maycomprise of a wired medium, wireless medium, or a combination ofwireless and wired media, including by way of non-limiting exampleEthernet, token ring, private or proprietary networks, among others.Client devices 118 through 120 may henceforth comprise one of manydevices, such as cellular phones, personal digital assistants (PDAs),computer devices or systems such as laptops, personal computers, set-topterminals, televisions with communication capabilities, DVD/CDrecorders, among others. Other networks are contemplated to be withinthe scope of the disclosure, including networks that use packetsincorporated with and/or compliant to other transport protocols orstandards.

The VDN system 200 may be implemented in hardware, software, firmware,or a combination thereof. To the extent certain embodiments of the VDNsystem 200 or a portion thereof are implemented in software or firmware,executable instructions for performing one or more tasks of the VDNsystem 200 are stored in memory or any other suitable computer readablemedium and executed by a suitable instruction execution system. In thecontext of this document, a computer readable medium is an electronic,magnetic, optical, or other physical device or means that can contain orstore a computer program for use by or in connection with a computerrelated system or method.

To the extent certain embodiments of the VDN system 200 or a portionthereof are implemented in hardware, the VDN system 200 may beimplemented with any or a combination of the following technologies,which are all well known in the art: a discrete logic circuit(s) havinglogic gates for implementing logic functions upon data signals, anapplication specific integrated circuit (ASIC) having appropriatecombinational logic gates, programmable hardware such as a programmablegate array(s) (PGA), a field programmable gate array (FPGA), etc.

Having described an example environment in which the VDN system 200 maybe employed, attention is directed to FIGS. 2A-2C, which compriseschematic diagrams that conceptually illustrate data flows and/orprocessing implemented by various example embodiments of VDN systems andmethods. Progressing from FIG. 2A to FIG. 2B and then to FIG. 2Crepresents a reduction in processing complexity, and hencelike-processing throughout these three figures are denoted with the samenumerical reference and an alphabetical or alphanumeric suffix (e.g., a,b, and c, or a-1, etc.) that may change from each figure for a givencomponent or diagram depending on whether there is a change or reductionin complexity to the component or represented system 200. Further, each“F” (e.g., F0, F1, etc.) shown above component 220 a in FIG. 2A (andlikewise shown and described in association with other figures) is usedto denote frames that have not yet been matched to the reference frame(F4), and each “M” (e.g., M0, M1, etc.) is used to denote frames thathave been matched (e.g., to the reference frame). Note that use of theterm “component” with respect to FIGS. 2A-2C does not imply thatprocessing is limited to a single electronic component, or that each“component” illustrated in these figures are necessarily separateentities. Instead, the term “component” in these figures graphicallyillustrates a given process implemented in the VDN system embodiments,and is used instead of “block,” for instance, to avoid confusion in theterm, block, when used to describe an image or pixel block.

Overall, VDN system embodiment, denoted 200 a in FIG. 2A, can besubdivided into frame matching 210 a, overlap block processing 250 a,and post processing 270 a. In frame matching 210 a, entire frames arematched at one time (e.g., a single time or single processing stage),and hence do not need to be matched during implementation of overlappedblock processing 250 a. In other words, block matching in the framematching process 210 a is decoupled (e.g., blocks are matched withoutblock overlapping in the frame matching process) from overlapped blockprocessing 250 a, and hence entire frames are matched and completed fora given video sequence before overlapped block processing 250 a iscommenced for the given video sequence. By decoupling frame matching 210a from overlapped block processing 250 a, block matching is reduced by afactor of sixty-four (64) when overlapped block processing 250 a has astep size of s=1 pixel in both the vertical and horizontal direction(e.g., when compared to integrating overlapped block processing 250 awith the frame matching 210 a). If the step size is s=2, then blockmatching is reduced by a factor of sixteen (16). One having ordinaryskill in the art should understand that various step sizes arecontemplated to be within the scope of the disclosure, the selection ofwhich is based on factors such as available computational resources andvideo processing performance (e.g., based on evaluation of PSNR, etc.).

Shown in component 220 a are eight (8) inputted contiguous frames, F0(t)through F7(t) (denoted above each symbolic frame as F0, F1, etc.). Theeight (8) contiguous frames correspond to a received raw video sequenceof plural frames. In other words, the eight (8) contiguous framescorrespond to a temporal sequence of frames. For instance, the frames ofthe raw video sequence are arranged in presentation output order (whichmay be different than the transmission order of the compressed versionsof these frames at the output of the headend 110). In some embodiments,different arrangements of frames and/or different applications arecontemplated to be within the scope of the disclosure. Note thatquantities fewer or greater than eight frames may be used in someembodiments at the inception of processing. Frame matching (e.g., toFIG. 4) is symbolized in FIGS. 2A-2C by the arrow head lines, such asrepresented in component 220 a (e.g., from F0 to F4, etc.). Asillustrated by the arrow head lines, frames F0(t) through F3(t) arematched to F4(t), meaning that blocks (e.g., blocks of pixels or imageblocks, such as 8×8, 8×4, etc.) have been selected from those frameswhich most closely match blocks in F4(t) through a motionestimation/motion compensation process as explained below. The result offrame matching is a set of frames M0(t) through M7(t), whereM4(t)=F4(t), as shown in component 230 a. Frames M0(t) through M7(t) areall estimates of F4(t), with M4(t)=F4(t) being a perfect match. M4(t)and F4(t) are used interchangeably herein.

Overlapped block processing 250 a is symbolically represented withcomponents 252 (also referred to herein as a group of matched noisyblocks or similar), 254 (also referred to herein as 3D denoising orsimilar), 256 (also referred to as a group or set of denoised blocks orsimilar), and 260 a (also referred to herein as pixel accumulationbuffer(s)). Overlapped block processing 250 a moves to a pixel locationi,j in each of the matched frames (e.g., the same, co-located, or commonpixel location), and for each loop, takes in an 8×8 noisy block b(i,j,t)(e.g., 240 a) from each matched frame M0 through M7, with the top leftcorner at pixel position i,j, so that b(i,j,t)=Mt(i:i+7, j:j+7). Notethat i,j are vertical and horizontal indices which vary over the entireframe, indicating the position in the overlapped processing loop. Forinstance, for a step size s=1, i,j takes on every pixel position in theframe (excluding boundaries of 7 pixels). For a step size s=2 i,j takeson every other pixel. Further note that 8×8 is used as an example blocksize, with the understanding that other block sizes may be used in someembodiments of overlapped block processing 250 a. The group of eight (8)noisy 8×8 blocks (252) is also denoted as b(i,j, 0:7). Note that theeight (8) noisy blocks b(i,j, 0:7) (252) are all taken from the samepixel position i,j in the matched frames, since frame alignment (as partof frame matching processing 210 a) is accomplished previously. Framematching 210 a is decoupled from overlapped block processing 250 a.

3D denoising 254 comprises forward and inverse transforming (e.g., 2Dfollowed by 1D) and thresholding (e.g., 1D and/or 2D), as explainedfurther below. In general, in 3D denoising 254, a 2D transform isperformed on each of the 8×8 noisy blocks (252), followed by a 1Dtransform across the 2D transformed blocks. After thresholding, theresult is inverse transformed (e.g., 1D, then 2D) back to pixel blocks.The result is a set of eight (8) denoised blocks bd(i,j, 0:7) (256).

For each loop, there are eight (8) denoised blocks (256), but in someembodiments, not all of the blocks of bd(i,j, 0:7) are accumulated tothe pixel accumulation buffers 260 a, as symbolically represented by theframes and blocks residing therein in phantom (dashed lines). Rather,the accumulation buffers 260 a comprise what is also referred to hereinas 2D+c accumulation buffers 260 a, where c represents an integer valuecorresponding to the number of buffers for corresponding frames ofdenoised blocks in addition to the buffer for A4. A 2D accumulationbuffer corresponds to only A4 (the reference frame) being accumulatedusing bd(i,j,4) (e.g., denoised blocks bd(i,j,4) corresponding to frameA4 are accumulated). In this example, another buffer corresponding toc=1 is shown as being accumulated, where the c=1 buffer corresponds todenoised blocks bd(i,j,7) corresponding to frame AM7. It follows thatfor an eight (8) frame window, a 2D+7 accumulation buffer equals a 3Daccumulation buffer. Further, it is noted that using a 2D+1 accumulationbuffer is analogous in the time dimension to using a step size s=4 inthe spatial dimension (i.e. the accumulation is decimated in time).Accordingly, c can be varied (e.g., from 0 to a defined integer) basedon the desired visual performance and/or available computationalresources. However, in some embodiments, a 3D accumulation buffercomprising denoised pixels from plural overlapped blocks is accumulatedfor all frames. In overlapped block processing 250 a, blocks bd(i,j,4)and bd(i,j,7) are accumulated in accumulation buffers 260 a at pixelpositions i,j since the accumulation is performed in the matched-framedomain, circumventing any need for inverse block matching within theoverlapped block processing loop 250 a. Further, uniform weighting(e.g., w(i,j)=1 or no weighting at all) for all denoised blocks isimplemented, which significantly reduces complexity. Note that in someembodiments, non-uniform weighting may be implemented. Note that in someembodiments, buffers for more than two frames (e.g., c>1) may beimplemented. For a 2D+1 buffer, a frame begins denoising when it becomesA7, since there is an accumulation buffer for A7 for the 2D+1accumulation buffer 260 a. A7 receives a second (2^(nd)) iteration ofdenoising when it becomes A4. The two are merged as shown inpost-processing 270 a, as explained further below.

From the accumulation buffers 260 a, post processing 270 a isimplemented, which comprises in one embodiment the processes of inverseframe matching 272 (e.g., as implemented in an inverse frame matchingmodule or logic), delay 274 (e.g., as implemented in a delay module orlogic), and merge and normalizing 276 (e.g., as implemented in merge andnormalize module or logic). Since in one embodiment the accumulationbuffer corresponding to AM7 is in the matched frame domain (e.g., frame7 matched to frame 4), after the overlapped block processing iscompleted, data flow advances to inverse frame matching 272 to inverseframe match AM7(t) to obtain A7(t). As noted, this operation occurs onceoutside of overlapped block processing 250 a. A7(t) is then delayed(274), in this example, three frames, and merged (added) and normalized276 to A4(t) (as represented by the dotted line arrowhead) to outputFD4(t), the denoised frame. Had the inverse frame matching 272 beenimplemented in the overlapped block processing 250 a, the inverse framematching would move a factor of sixty-four (64) more blocks than theimplementation shown for a step size s=1, or a factor of 16 more fors=2.

Ultimately, after the respective blocks for each accumulated frame havebeen accumulated from plural iterations of the overlapped blockprocessing 250 a, the denoised and processed frame FD4 is output to theencoder or other processing devices in some embodiments. As explainedfurther below, a time shift is imposed in the sequence of framescorresponding to frame matching 210 a whereby one frame (e.g., F0) isremoved and an additional frame (not shown) is added for frame matching210 a and subsequent denoising according to a second or subsequenttemporal frame sequence or temporal sequence (the first temporalsequence associated with the first eight (8) frames (F0-F7) discussed inthis example). Accordingly, after one iteration of frame processing(e.g., frame matching 210 a plus repeated iterations or loops ofoverlapped block processing 250 a), as illustrated in the example ofFIG. 2A, FD4(t) is output as a denoised version of F4(t). As indicatedabove, all of the frames F0(t) through F7(t) shift one frame (alsoreferred to herein as time-shifted) so that F0(t+1)=F1(t),F1(t+1)=F2(t), etc., and a new frame F7(t+1) (not shown) enters the“window” (component 220 a) of frame matching 210 a. Note that in someembodiments, greater numbers of shifts can be implemented to arrive atthe next temporal sequence. Further, F0(t) is no longer needed at t+1 soone frame leaves the window (e.g., the quantity of frames outlined incomponent 220 a). For the 8-frame case, as one non-limiting example,there is a startup delay of eight (8) frames, and since three (3) futureframes are needed to denoise F4(t) and F5 through F7, there is generaldelay of three (3) frames.

Referring now to FIG. 2B, shown is a VDN system embodiment, denoted 200b, with further reduced computational complexity compared to the VDNsystem embodiment 200 a illustrated in FIG. 2A. The simplification inFIG. 2B is at least partially the result of the 2D+1 accumulationbuffers 260 a and a modified 1D temporal transform, as explained furtherbelow. In the above-description of the 2D+1 accumulation buffers 260 ain FIG. 2A, it is noted that the 2D+1 accumulation buffers 260 a requireonly buffers (e.g., two) for denoised blocks, bd(i,j,4) and bd(i,j,7).Accordingly, a further reduction in complexity includes the “collapse”of the left four (4) frames, F0 through F3, into a singlesummation-frame FSUM, and frame-matching the summation-frame to F4, asillustrated in frame matching 210 b, and in particular, component 220 bof FIG. 2B. The collapse to a single summation frame comprises anoperation which represents a close approximation to the left-hand sideframe matching 210 a illustrated in FIG. 2A. Since F0 through F3 werepreviously matched together at time t−4, no matching operations areneeded on those individual frames. Instead, frame matching 210 b matchesthe sum, FSUM, from time t−4 to F4, where

$\begin{matrix}{{{FSUM}\left( {t - 4} \right)} = {{\sum\limits_{j = 4}^{7}{{{Mj}\left( {t - 4} \right)}{{FSUM}\left( {t - 4} \right)}}} = {\sum\limits_{j = 4}^{7}{{{Mj}\left( {t - 4} \right)}.}}}} & {{Eq}.\mspace{14mu} (1)}\end{matrix}$

Eq. (1) implies that FSUM(t) is the sum of the four (4) frame-matched F4through F7 frames, denoted M4(t) through M7(t) at time t, and is usedfour (4) frames (t−4) later as the contribution to the left (earliest)four (4) frames in the 3D transforms. A similar type of simplificationis made with respect to frames F5 and F6. After the four (4) frames F0through F3 are reduced to one FSUMframe, and F5 and F6 are reduced to asingle frame, and then matched to F4(t), there are only four (4) matchedframes, MSUM(actually MSUM0123), M4, M5+6, and M7, as noted in component230 b, and therefore the entire overlapped block processing 250 bproceeds using just four (4) (b(i,j,4:7)) matched frames. The number offrames in total that need matching to F4(t) is reduced to three (3) inthe VDN system embodiment 200 b of FIG. 2B from seven (7) in the VDNsystem embodiment 200 a in FIG. 2A. In other words, overlapped blockprocessing 250 b receives as input the equivalent of eight (8) frames,hence obtaining the benefit of eight (8) frames using a fewer number offrame estimates.

FIG. 2C is a block diagram of a VDN system embodiment, 200 c, thatillustrates a further reduction in complexity from the systemembodiments in FIGS. 2A-2B, with particular emphasis at the accumulationbuffers denoted 260 b. In short, the accumulation buffer 260 b comprisesa 2D accumulation buffer, which eliminates the need for inverse motioncompensation and reduces the complexity of the post-accumulatingprocessing 270 b to a normalization block. In this embodiments, theframes do not need to be rearranged into the frame-matching (e.g.,component 220 b) as described above in association with FIG. 2B.Instead, as illustrated in the frame matching 210 c, F7 is matched toF6, and the result is added together to obtain FSUM67. F5 is matched toF4 using the motion vectors from two (2) frames earlier (when F5, F4were positioned in time at F7, F6 respectively), so this manner of framematching is shown as a dotted line between F4 and F5 in component 220 cin FIG. 2C. As before, FSUM0123 represents four (4) frames matched, four(4) frames earlier, and summed together. In summary, frame matching 210c for the 2D accumulation buffer 260 b matches three (3) frames to F4:FSUM0123, FSUM67 and F5.

Having described conceptually example processing of certain embodimentsof VDN systems 200, attention is now directed to FIG. 3, which comprisesa block diagram of VDN system embodiment 200 c-1. It is noted that thearchitecture and functionality described hereinafter is based on VDNsystem embodiment 200 c described in association with FIG. 2C, with theunderstanding that similar types of architectures and components for VDNsystem embodiments 200 a and 200 b can be derived by one having ordinaryskill in the art based on the teachings of the present disclosurewithout undue experimentation. VDN system embodiment 200 c-1 comprises aframe alignment module 310, an overlapped block processing module 350,an accumulation buffer 360, and a normalization module 370(post-accumulation processing). It is noted that frame processing 250 inFIGS. 2A-2C correspond to the processing implemented by the framealignment module 310, and overlap block processing 250 corresponds tothe processing implemented by the overlapped block processing module350. In addition, the 2D accumulation buffer 360 and the normalizationmodule 370 implement processing corresponding to the components 260 and270, respectively, in FIG. 2C. Note that in some embodiments,functionality may be combined into a single component or distributedamong more or different modules.

As shown in FIG. 3, the frame alignment module 310 receives plural videoframes F4(t), F6(t), and F7(t), where F4(t) is the earliest frame intime, and t is the time index which increments with each frame. Theframe FSUM0123(t)=FSUM4567(t−4) represents the first four (4) framesF0(t) through F3(t) (F4(t−4) through F7(t−4)) which have been matched toframe F0(t) (F4(t−4)) previously at time t=t−4. Note that for interlacedvideo, the frames may be separated into fields and the VDN systemembodiment 200 c-1 may be run separately (e.g., separate channels, suchas top channel and bottom channel) on like-parity fields (e.g., top orbottom), with no coupling, as should be understood by one havingordinary skill in the art in the context of the present disclosure.Throughout this disclosure, the term “frame” is used with theunderstanding that the frame can in fact be an individual field with nodifference in processing. The frame alignment module 310 produces thefollowing frames for processing by the overlapped block processingmodule 350, the details of which are described below: M4(t), MSUM67(t),M5(t), and MSUM0123(t).

Before proceeding with the description of the overlapped blockprocessing module 350, example embodiments of the frame alignment module310 are explained below and illustrated in FIGS. 4A and 4B. One exampleembodiment of a frame alignment module 310 a, shown in FIG. 4A, receivesframes F4(t), F6(t), F7(t), and FSUM0123(t). It is noted that M5(t) isthe same as M76(t−2), which is the same as M54(t). F7(t) isframe-matched to F6(t) at frame match module 402, producing M76(t).After a delay (404) of two (2) frames, M76(t) becomes M54(t), which isthe same as M5(t). Note that blocks labeled “delay” and shown in phantom(dotted lines) in FIGS. 4A-4B are intended to represent delays imposedby a given operation, such as access to memory. F6(t) is summed withM76(t) at summer 406, resulting in FSUM67(t). FSUM67(t) is frame-matchedto F4(t) at frame match module 408, producing MSUM67(t). MSUM67(t) ismultiplied by two (2) and added to F4(t) and M5(t) at summer 410,producing FSUM4567(t). FSUM4567(t) can be viewed as frames F5(t) throughF7(t) all matched to F4(t) and summed together along with F4(t).FSUM4567(t) is delayed (412) by four (4) frames producing FSUM0123(t)(i.e., FSUM4567(t−4)=FSUM0123(t)). FSUM0123(t) is frame-matched to F4(t)at frame match module 414 producing MSUM0123(t). Accordingly, the outputof the frame alignment module 310 a comprises the following frames:MSUM0123(t), M4(t), M5(t), and MSUM67(t).

One having ordinary skill in the art should understand in the context ofthe present disclosure that equivalent frame processing to thatillustrated in FIG. 4 may be realized by the imposition of differentdelays in the process, and hence use of different time sequences offrames in a given temporal sequence as the input. For instance, as shownin FIG. 4B, for frame alignment module 310 b, an extra frame delay 416corresponding to the derivation of frames F6(t) and F7(t) from FSUM87(t)may be inserted, resulting in frames FSUM67(t). All other modulesfunction are as explained above in association with FIG. 4A, and henceomitted here for brevity. Such a variation to the embodiment describedin association with FIG. 4A enables all frame-matching operations towork out of memory.

One example embodiment of a frame match module, such as frame matchmodule 402, is illustrated in FIG. 5. It should be understood that thediscussion and configuration of frame match module 402 similarly appliesto frame match modules 408 and 414, though not necessarily limited toidentical configurations. Frame match module 402 comprises motionestimation (ME) and motion compensation (MC) functionality (alsoreferred to herein as motion estimation logic and motion compensationlogic, respectively), which is further subdivided into luma ME 502 (alsoluma ME logic or the like), chroma ME 504 (also chroma ME logic or thelike), luma MC 506 (also luma MC logic or the like), and chroma MC 508(also chroma MC logic or the like). In one embodiment, the luma ME 502comprises a binomial filter 510 (also referred to herein as a pixelfilter logic), a decimator 512 (also referred to herein as decimatorlogic), a decimated block matching (DECBM) module 514 (also referred toherein as decimated block matching logic), a full pixel bock matching(BM) module 516 (also referred to herein as full pixel block matchinglogic), and a luma refinement BM module 518. The chroma ME 504 comprisesa chroma refinement BM module 520. The luma MC 506 comprises a luma MCmodule 522, and the chroma MC 508 comprises a chroma MC module 524.

The frame matching module 402 takes as input two video frames, each ofwhich includes luminance and chrominance data in either of the wellknown CCIR-601 4:2:0 or 4:2:2 formats, though not limited to theseformats, and in some embodiments may receive a proprietary format amongother types of formats. For 4:2:0 formats, the chrominance includes twochannels subsampled by a factor of two (2) in both the vertical andhorizontal directions. For 4:2:2 formats, the chrominance is subsampledin only the horizontal direction. The luminance inputs are denoted inFIG. 5 as LREF and LF, which represent the reference frame luminance anda frame luminance to match to the reference, respectively. Similarly,the corresponding chrominance inputs are denoted as CREF (reference) andCF (frame to match to the reference). The output of the frame matchprocess is a frame which includes luminance (LMAT) data and chrominance(CMAT) data. For instance, according to the embodiments described inassociation with, and illustrated in, FIGS. 2C, 3A, and 5, LREF, CREF,LF, CF and LMAT, CMAT correspond to the sets of frames given in Table 1below:

TABLE 1 Sets of Frames Undergoing Frame Matching LREF, CREF LF, CF LMAT,CMAT Description F6(t) F7(t) M76(t) Frame Match F7(t) to F6(t) M76(t) isan estimate of F6(t) from F7(t) F4(t) FSUM67(t) MSUM67(t) Frame MatchFSUM67(t) to F4(t) Normalize FSUM67(t) with divide by 2 prior to FrameMatch. MSUM67(t) is an estimate of F4(t) from both F6(t) and F7(t) F4(t)FSUM0123(t) MSUM0123(t) Frame Match FSUM0123(t) to F4(t) NormalizeFSUM(t-4) with divide by 4 prior to Frame Match. MSUM0123(t) is anestimate of F4(t) from F0(t), F1(t), F2(t), F3(t) (or equivalently,F4(t-4), F5(t-4), F6(t-4), F7(t-4))

In general, one approach taken by the frame match module 402 is toperform block matching on blocks of pixels (e.g., 8×8) in the luminancechannel, and to export the motion vectors from the block matching of theluminance channel for re-use in the chrominance channels. In oneembodiment, the reference image LREF is partitioned into a set of 8×8non-overlapping blocks. The final result of frame-matching is a set ofmotion vectors into the non-reference frame, LF, for each 8×8 block ofLREF to be matched. Each motion vector represents the 8×8 block ofpixels in LF which most closely match a given 8×8 block of LREF. Theluminance pixels are filtered with a binomial filter and decimated priorto block matching.

The luma ME 502 comprises logic to provide the filtering out of noise,coarse block matching (using a multi-level or multi-stage hierarchicalapproach that reduces computational complexity) of the filtered blocks,and refined block matching using undecimated pixel blocks and a finalmotion vector derived from candidates of the coarse block matchingprocess and applied to unfiltered pixels of the inputted frames.Explaining in further detail and with reference to FIG. 5, the luminanceinput (LREF, LF) is received at binomial filter 510, luma refinement BMmodule 518, and luma MC module 522. The binomial filter 510 processesthe data and produces full-pixel luminance (BF_LF, BF_LREF), each of theluminance images of size N_(ver)×N_(hor). The binomial filter 510performs a 2D convolution of each input frame according to the followingequation:

$\begin{matrix}{{{{BF\_ X}\left( {i,j} \right)} = {\sum\limits_{m = {- 1}}^{1}{\sum\limits_{n = {- 1}}^{1}{{x\left( {m,n} \right)}{G\left( {{i - m},{j - n}} \right)}}}}},} & {{Eq}.\mspace{14mu} (2)}\end{matrix}$

where x(0: N_(ver)−1, 0: N_(hor)−1) is an input image of sizeN_(ver)×N_(hor), BF_X(i,j) is the binomial filtered output image, andG(m,n) is the 2D convolution kernel given by the following equation:

$\begin{matrix}{{G\left( {i,j} \right)} = {\frac{1}{16}\begin{pmatrix}1 & 2 & 1 \\2 & 4 & 2 \\1 & 2 & 1\end{pmatrix}}} & {{Eq}.\mspace{14mu} (3)}\end{matrix}$

Accordingly, LF and LREF are both binomial filtered according to Eq. (3)to produce BF_LF and BF_LREF, respectively, which are also input to thedecimator 512 and the full BM module 516. Although a binomial filter isdescribed as one example pixel filter, in some embodiments, other typesof filters may be employed without undue experimentation, as should beunderstood by one having ordinary skill in the art.

BF_LF and BF_LREF are received at decimator 512, which performs, in oneembodiment, a decimation by two (2) function in both the vertical andhorizontal dimensions to produce BF_LF2 and BF_LREF2, respectively. Theoutput of the decimator 512 comprises filtered, decimated luminance data(BF_LF2, BF_LREF2), where each of the luminance images are of sizeN_(ver)/2×N_(hor)/2. Thus, if the size of LF and LREF are bothN_(ver)×N_(hor) pixels, then the size of BF_LF2 and BF_LREF2 areN_(ver)/2×N_(hor)/2 pixels. In some embodiments, other factors orfunctions of decimation may be used, or none at all in some embodiments.

The decimated, binomial-filtered luma pixels, BF_LF2 and BF_LREF2, areinput to the DECBM module 514, which performs decimated block matchingon the filtered, decimated data (BF_LF2, BF_LREF2). In one embodiment,the DECBM module 514 applies 4×4 block matching to the 4×4 blocks ofBF_LREF2 to correspond to the 8×8 blocks of BF_LREF. In other words, the4×4 pixel blocks in the decimated domain correspond to 8×8 blocks in theundecimated domain. The DECBM module 514 partitions BF_LREF2 into a setof 4×4 blocks given by the following equation:

$\begin{matrix}{{{{{BREF}\; 2\left( {i,j} \right)} = {{BF\_ LREF}\; 2\left( {{{4i\text{:}\mspace{14mu} 4i} + 3},{{4j\text{:}\mspace{14mu} 4j} + 3}} \right)}},{where}}{{i = 0},1,{{\ldots \mspace{14mu} \frac{N_{hor} - 1}{4}\mspace{14mu} {and}\mspace{14mu} j} = 0},1,{\ldots \mspace{14mu} {\frac{N_{ver} - 1}{4}.}}}} & {{Eq}.\mspace{14mu} (4)}\end{matrix}$

It is assumed that BF_LREF2 is divisible by four (4) in both thevertical and horizontal dimensions. The set of 4×4 blocks BREF2(i,j) inEq. (4) includes all pixels of BF_LREF2 partitioned as non-overlapping4×4 blocks. A function of the DECBM module 514 is to match each of theseblocks to the most similar blocks in BF_LF2.

For each of the 4×4 blocks at BREF2(i,j), the DECBM module 514 searches,in one example embodiment, over a ±24 horizontal by ±12 vertical searcharea of BF_LF2 (for a total 49×25 decimated pixel area) to find 4×4pixel blocks which most closely match the current 4×4 block. In someembodiments, differently configured (e.g., other than 24×12) searchareas are contemplated. The search area of BF_LF2 is co-located with theblock BREF2(i,j) to be matched. In other words, the search regionBF_LF2_SEARCH(i, j) may be defined by the following equation:

BF _(—) LF2_SEARCH(i, j)=BF _(—) LF2(4i:4i±12, 4j:4j±24)   Eq. (5)

Eq. (5) defines the search region as a function of (i j) that iscentered at the co-located block BF_LF2(4 i,4 j) as in BF_LREF2(4 i,4j), or equivalently BREF2(i,j). The search region may be truncated forblocks near the borders of the frame where a negative or positive offsetdoes not exist. Any 4×4 block at any pixel position inBF_LF2_SEARCH(i,j) is a candidate match. Therefore, the entire searcharea is traversed extracting 4×4 blocks, testing the match, then movingone (1) pixel horizontally or one (1) pixel vertically. This operationis well-known to those with ordinary skill in the art as “full search”block matching, or “full search” motion estimation.

One matching criterion, among others in some embodiments, is defined asa 4×4 Sum-Absolute Difference (SAD) between the candidate block inBF_LF2 search area and the current BREF2 block to be matched accordingto Eq. 6 below:

${{SAD}\; 4x\; 4\left( {y,x} \right)} = {\sum\limits_{u = 0}^{3}{\sum\limits_{v = 0}^{3}{\begin{matrix}{{{BF\_ LF2}\left( {{{4i} + y + u},{{4j} + x + v}} \right)} -} \\{{BREF}\; 2\left( {{i + u},{j + v}} \right)}\end{matrix}}}}$

(Eq. (6)), where −24≦x≦24, −12≦y≦12. The values of y and x whichminimize the SAD 4×4 function in Eq. (6) define the best matching blockin BF_LF2. The offset in pixels from the current BREF2 block in thevertical (y) and horizontal (x) directions defines a motion vector tothe best matching block. If a motion vector is denoted by mv, then mv.ydenotes the motion vector vertical direction, and mv.x denotes thehorizontal direction. Note that throughout the present disclosure,reference is made to SAD and SAD computations for distance or differencemeasures. It should be understood by one having ordinary skill in theart that other such difference measures, such as sum-squared error(SSE), among others well-known to those having ordinary skill in the artcan be used in some embodiments, and hence the example embodimentsdescribed herein and/or otherwise contemplated to be within the scope ofthe disclosure are not limited to SAD-based difference measures.

In one embodiment, the DECBM module 514 may store not only the bestmotion vector, but a set of candidate motion vectors up toN_BEST_DECBM_MATCHES, where N_BEST_DECBM_MATCHES is a parameter havingan integer value greater than or equal to one. For example, in oneimplementation, N_BEST_DECBM_MATCHES=3. As the DECBM module 514traverses the search area, computing SAD 4×4 according to Eq. (6), theDECBM module 514 keeps track of the N_BEST_DECBM_MATCHES (e.g., minimumSAD 4×4 blocks) by storing the motion vectors (x and y values)associated with those blocks and the SAD 4×4 value. In one embodiment, ablock is only included in the N_BEST_DECBM_MATCHES if its distance ineither of the horizontal or vertical directions is greater than one (1)from any other saved motion vectors. The output of the DECBM module 514is a set of motion vectors (MV_BEST) corresponding toN_BEST_DECBM_MATCHES, the motion vectors input to the full pixel BMmodule 516. In some embodiments, in addition to MV_BEST, the DECBMmodule 514 adds one or more of the following two motion vectorcandidates, if they are not already in the MV_BEST set: a zero motionvector and/or a motion vector of a neighboring block (e.g., a blocklocated one row above). For example, if N_BEST_DECBM_MATCHES=3, meaningthree (3) candidate motion vectors come from the DECBM module 514, thenthe total candidate motion vectors is five (5) (three (3) from the DECBMmodule SAD 4×4 operation plus a zero motion vector plus a neighboringmotion vector). Therefore, in this example, if N_BEST_DECBM_MATCHES=3,then the total candidate motion vectors is five (5).

The DECBM module 514 omits from a search the zero vector (mv.x=0,mv.y=0) as a candidate motion vector output and any motion vectorswithin one pixel of the zero vector since, in one embodiment, the zerovector is always input to the next stage processing in addition to thecandidate motion vectors from the DECBM module 514. Therefore, ifN_BEST_DECBM_MATCHES=3, then the three (3) motion vectors consist ofnon-zero motion vectors, since any zero motion vectors are omitted fromthe search. Any zero-motion vector is included as one of the addedmotion vectors of the output of the DECBM module 514. In someembodiments, zero vectors are not input to the next stage and/or are notomitted from the search.

The full pixel BM module 516 receives the set of candidate motionvectors, MV_BEST, and performs a limited, full pixel block matchingusing the filtered, undecimated frames (BF_LF, BF_LREF). In other words,the full pixel BM module 516 takes as input the motion vectors obtainedin the DECBM module-implemented process, in addition to the zero motionvector and the motion vector from a neighboring block as explainedabove, and chooses a single refined motion vector from the candidateset. In some embodiments, a neighboring motion vector is not included asa candidate.

Operation of an embodiment of the full pixel BM module 516 is describedas follows. The full pixel BM module 516 partitions BF_LREF into 8×8blocks corresponding to the 4×4 blocks in BF_REF2 as follows:

$\begin{matrix}{{{{BREF}\left( {i,j} \right)} = {{BF\_ LREF}\left( {{{8i\text{:}\mspace{14mu} 8i} + 7},{{8j\text{:}\mspace{14mu} 8j} + 7}} \right)}},{{{where}\mspace{14mu} i} = 0},1,{\ldots \mspace{14mu} \frac{N_{hor} - 1}{8}},{{{and}\mspace{14mu} j} = 0},1,{\ldots \mspace{14mu} {\frac{N_{ver} - 1}{8}.}}} & {{Eq}.\mspace{14mu} (7)}\end{matrix}$

BREF is a set of non-overlapping 8×8 blocks comprising the entireluminance frame BF_LREF with a direct correspondence to the BREF2 4×4blocks. The full pixel BM module 516 receives the MV_BEST motion vectorsfrom the DECBM module 514 and the full pixel (undecimated), binomialfiltered luminance BF_LREF and BF_LF.

The input, MV_BEST, to the full pixel BM module 516, may be denotedaccording to the following set: MV_BEST={mv_best(0), mv_best(1), . . .mv_best(N_BEST_DECBM_MATCHES−1)}. The full pixel BM module 516 scalesthe input motion vectors to full pixel by multiplying the x and ycoordinates by two (2), according to Eq. (8) as follows:

mvfull(k).x=2×mv_best(k).x 0≦k<N_BEST_DECBM_MATCHES,

mvfull(k).y=2×mv_best(k).y 0≦k<N_BEST_DECBM_MATCHES   (Eq. (8))

where 0≦k≦N_BEST_DECBM_MATCHES−1. Note that the zero motion vector andneighboring block motion vector do not need scaling since zero does notneed scaling, and the neighboring block motion vector is already scaled(sourced from the full pixel BM module 516). After scaling to fullpixel, the full pixel BM module 516 determines a refined motion vector,mvrfull(k), from its corresponding candidate motion vector, mvfull(k),by computing a minimum SAD for 8×8 full-pixel blocks in a 5×5 refinementsearch around the scaled motion vector according to Eq. (9):

$\begin{matrix}{{{SAD}\; 8x\; 8\left( {i,j,{{{{mvfull}(k)} \cdot y} + m},{{{{mvfull}(k)} \cdot x} + n}} \right)} = {\sum\limits_{u = 0}^{7}{\sum\limits_{v = 0}^{7}{\begin{matrix}{{{BF\_ LF}\begin{pmatrix}{{{8i} + {{{mvfull}(k)} \cdot y} + n + u},} \\{{8j} + {{mvfull}{(k) \cdot x}} + m + v}\end{pmatrix}} -} \\{{BREF}\left( {{i + u},{j + v}} \right)}\end{matrix}}}}} & {{Eq}.\mspace{14mu} (9)}\end{matrix}$

where −2≦m≦2, −2≦n≦2. Note that one having ordinary skill in the artshould understand in the context of the present disclosure thatrefinement search ranges other than 5×5 are possible and hencecontemplated for some embodiments. Minimizing Eq. (9) for each motionvector of the candidate set results in (N_BEST_DECBM_MATCHES+2) refinedcandidate motion vectors, mvrfull(k), where0≦k<(N_BEST_DECBM_MATCHES+2). The full pixel BM module 516 selects afinal winning motion vector from the refined motion vectors by comparingthe SAD of the refined motion vectors according to the followingequation:

$\begin{matrix}{\lbrack{kf}\rbrack = {\underset{k}{Min}\begin{Bmatrix}{{\lambda*{MVDIST}(k)} + {{DISTBIAS}(k)} +} \\{{SAD}\; 8x\; 8\begin{pmatrix}{i,j,{{{mvrfull}(k)} \cdot y},} \\{{{mvrfull}(k)} \cdot x}\end{pmatrix}}\end{Bmatrix}}} & {{Eq}.\mspace{14mu} (10)}\end{matrix}$

where:

-   kf=index of refined motion vector that is the winner;-   MVDIST(k)=min(dist(mvrfull(k), 0), dist(mvrfull(k), mvfull(B)));-   mvrfull(B)=winning motion vector of neighboring block;-   dist(a,b)=distance between motion vectors a and b;-   min(x,y)=minimum of x and y;

$\begin{matrix}{{{{DISTBIAS}(k)} = {{0\mspace{14mu} {for}\mspace{14mu} {{MVDIST}(k)}} < 12}},} \\{{{{20\mspace{14mu} {for}\mspace{14mu} {{MVDIST}(k)}} < 20},{40\mspace{14mu} {otherwise}\mspace{14mu} \bullet}}} \\{{= {{operational}\mspace{14mu} {parameter}}},{{e.g.\mspace{14mu} \bullet} = 4}}\end{matrix}$

In other words, the larger the motion vector, the lower the SAD value tojustify the larger motion vector as a winning candidate. For instance,winners comprising only marginally lower SAD values likely results inrandom motion vectors. The 12, 20, 40 values described above forces anincreased justification (lower SAD values) for increasingly largermotion vectors. In some embodiments, other values and/or other relativedifferences between these values may be used (e.g., 1,2,3 or 0, 10, 20,etc.]. Therefore, the final motion vector result from the full pixelblock mode operation of full pixel BM module 516 is given by:

mvf(i, j).x=mvrfull(kf).x

mvf(i, j).y=mvrfull(kf).y   Eq. (11)

If the SAD value in Eq. (9) corresponding to the best motion vector ofEq. (11) is above a threshold, T_SAD, the block is flagged as a badblock (e.g., BAD_MC_BLOCK) so that instead of copying the blockindicated by the motion vector in the search frame, the motioncompensation process (described below) copies the original block of thereference frame instead.

The resultant output of the full pixel BM module 516 comprises a singlefinal motion vector, MVF, for each non-overlapping block, which is inputto the luma refinement BM module 518. The luma refinement BM module 518(like the full pixel BM module 516) uses 8×8 block matching since itreceives as input full (non-decimated) images. That is, the lumarefinement BM module 518 refines the motion vectors using originalunfiltered frame data (LREF, LF), or more specifically, takes as inputthe set of motion vectors MVF obtained in the full pixel BM module 516and refines the motion vectors using original unfiltered pixels.Explaining further, the luma refinement BM module 518 partitions theoriginal noisy LREF into 8×8 non-overlapping blocks corresponding to the8×8 blocks in BF_REF according to the following equation:

$\begin{matrix}{{{{{REF}\left( {i,j} \right)} = {{LREF}\left( {{{8i\text{:}\mspace{14mu} 8i} + 7},{{8j\text{:}\mspace{14mu} 8j} + 7}} \right)}},{where}}\mspace{14mu} {{i = 0},1,{\ldots \mspace{14mu} \frac{N_{hor} - 1}{8}},{{{and}\mspace{14mu} j} = 0},1,{\ldots \mspace{14mu} {\frac{N_{ver} - 1}{8}.}}}} & {{Eq}.\mspace{14mu} (12)}\end{matrix}$

REF is a set of non-overlapping 8×8 blocks comprising the entireluminance frame LREF with a direct correspondence to the BREF 8×8blocks. For each block to be matched in REF, there is a motion vectorfrom full pixel block mode operation of full pixel BM module 516 (e.g.,mvf(i,j)). In one embodiment, a 1-pixel refinement around mvf(i,j)proceeds by the m and n which minimizes the following equation:

$\begin{matrix}{{{SAD}\; 8x\; 8\begin{pmatrix}{i,j,{{{{mvf}\left( {i,j} \right)} \cdot y} + m},} \\{{{mvf}{\left( {i,j} \right) \cdot x}} + n}\end{pmatrix}} = {\sum\limits_{u = 0}^{7}{\sum\limits_{v = 0}^{7}{\begin{matrix}{{{LF}\begin{pmatrix}{{{8i} + {{{mvf}\left( {i,j} \right)} \cdot y} + u + n},} \\{{8j} + {{{mvf}\left( {i,j} \right)} \cdot x} + v + m}\end{pmatrix}} -} \\{{REF}\left( {{i + u},{j + v}} \right)}\end{matrix}}}}} & {{Eq}.\mspace{14mu} (13)}\end{matrix}$

where −1≦m≦−1, −1≦n≦−1. In some embodiments, pixel refinement other thanby one (1-pixel) may be used in some embodiments, or omitted in someembodiments. The refined motion vector for the block at position i,j isgiven by the values of m and n, mref and nref respectively, whichminimize Eq. (13). The refined motion vector is given by Eq. (14) asfollows:

$\begin{matrix}{{{{{mvr}\left( {i,j} \right)} \cdot x} = {{{{mvf}\left( {i,j} \right)} \cdot x} + {mref}}}{{{{mvr}\left( {i,j} \right)} \cdot x} = {{{{mvf}\left( {i,j} \right)} \cdot x} + {mref}}}{{{{mvr}\left( {i,j} \right)} \cdot y} = {{{{mvf}\left( {i,j} \right)} \cdot x} + {nref}}}{{{{mvr}\left( {i,j} \right)} \cdot y} = {{{{mvf}\left( {i,j} \right)} \cdot x} + {nref}}}{{{{where}\mspace{14mu} i} = 0},1,{\ldots \mspace{14mu} \frac{N_{hor} - 1}{8}},{{{and}\mspace{14mu} j} = 0},1,{\ldots \mspace{14mu} {\frac{N_{ver} - 1}{8}.}}}} & {{Eq}.\mspace{14mu} (14)}\end{matrix}$

MVRL (also referred to herein as refined motion vector(s)) denotes thecomplete set of refined motion vectors mvr(i,j) (e.g., for i=0, 8, 16, .. . N_(ver)−7;j=0, 8, 16, . . . N_(Hor)−7) for the luminance channeloutput from the luma refinement BM module 518. In other words, MVRLdenotes the set of motion vectors representing every non-overlapping 8×8block of the entire frame.

MVRL is used by the luma MC module 522 and the chroma refinement BMmodule 520. Referring to the chroma refinement BM module 520, therefined motion vectors, MVRL, are received at the chroma refinement BMmodule 520, which performs a refined block matching in the chrominancechannel based on inputs CF and CREF. For 4:2:0 video formats, when thechroma is sub-sampled by a factor of two (2) in each of the horizontaland vertical dimensions, the chroma refinement BM module 520 performs4×4 full-pixel block matching. That is, the chroma (both Cb and Cr) 4×4blocks correspond directly to the luma 8×8 blocks. Using the MVRL input,the chroma refinement BM module 520, for both Cb and Cr chroma frames,performs a 1-pixel refinement around the MVRL input motion vectors in asimilar manner to the process performed by the luma refinement BM module518 described above, but using 4×4 blocks instead of 8×8, and a SAD 4×4instead of SAD 8×8 matching criterion. The resulting set of motionvectors are MVRCb for Cb and MVRCr for Cr (collectively shown as MVRC inFIG. 5), which are input to the chroma MC module 524 to perform motioncompensation.

Motion compensation is a well-known method in video processing toproduce an estimate of one frame from another (i.e., to “match” oneframe to another). MVRL and MVRC are input to motion compensation (MC)processes performed at luma MC module 522 and chroma MC module 524,respectively, which import the blocks indicated by the MVRL and MVRCmotion vectors. With reference to the luma MC module 522, after theblock matching has been accomplished as described hereinabove, the LFframe is frame-matched to LREF by copying the blocks in LF indicated bythe motion vectors MVRL. For each block, if the block has been flaggedas a BAD_MC_BLOCK by the full pixel BM module 516, instead of copying ablock from the LF frame, the reference block in LREF is copied instead.For chroma operations, the same process is carried out in chroma MCmodule 508 on 4×4 blocks using MVRCb for Cb and MVRCr for Cr, and hencediscussion of the same is omitted for brevity. Note that 8×8 and 4×4were described above for the various block sizes, yet one havingordinary skill in the art should understand that in some embodiments,other block sizes than those specified above may be used.

With reference to FIG. 3, having described an example embodiment of thevarious modules or logic that comprise the frame alignment module 310for the VDN system embodiment 200 c-1, attention is directed to theoverlapped block processing module 350. In general, after framematching, the overlapped block processing module 350 denoises theoverlapped 3D blocks and accumulates the results, as explained inassociation with FIGS. 2A-2C. In one embodiment, looping occurs bystepping j by a step size s in pixels (e.g. s=2). The overlapped blockprocessing moves horizontally until j==N_(Hor)−1, after which j is setto 0 and i is incremented by s. Note that in some embodiments, seven (7)pixels are added around the border of the frame to enable the borderpixels to include all blocks. For simplicity, these pixels may be a DCvalue equal to the touching border pixel.

Referring again to FIG. 3, the overlapped block processing module 350receives the matched frames M4(t), M5(t), MSUM67(t), and MSUM0123(t),and extracts co-located blocks of 8×8 pixels from these four (4) frames.Denote the four (4) blocks extracted at a particular i,j pixel positionin the four (4) frames as b(i,j,t) with t=0 . . . 3, then afterreordering of the blocks (as explained below in the context of the 1Dtransform illustrated in FIGS. 6A-6D), the following terminology isdescribed:

-   b(i,j,0) is an 8×8 block from MSUM0123(t) at pixel position i,j-   b(i,j,1) is an 8×8 block from M4(t) at pixel position i,j-   b(i,j,2) is an 8×8 block from M5(t) at pixel position i,j-   b(i,j,3) is an 8×8 block from MSUM67(t) at pixel position i,j

Starting with i=0 and j=0, the upper left corner of the frames, a 2Dtransform module 304 (also referred to herein as transform logic)extracts the four (4) blocks b(0,0,0:3) and performs a 2D transform.That is, the 2D transform is taken on each of the four (4) temporalblocks b(i,j,t) with 0≦t≦3, where i,j is the pixel position of the topleft corner of the 8×8 blocks in the overlapped block processing. Insome embodiments, a 2D-DCT, DWT, among other well-known transforms maybe used as the spatial transform. In an embodiment described below, the2D transform is based on an integer DCT defined in the Advanced VideoCoding (AVC) standard (e.g., an 8×8 AVC-DCT), which has the followingform:

$\begin{matrix}{{{H(X)} = {{{DCT}(X)} = {C \cdot X \cdot C^{T}}}}{{where},{C = {\begin{bmatrix}8 & 8 & 8 & 8 & 8 & 8 & 8 & 8 \\12 & 10 & 6 & 3 & {- 3} & {- 6} & {- 10} & {- 12} \\8 & 4 & {- 4} & {- 8} & {- 8} & {- 4} & 4 & 8 \\10 & {- 3} & {- 12} & {- 6} & 6 & 12 & 3 & {- 10} \\8 & {- 8} & {- 8} & 8 & 8 & {- 8} & {- 8} & 8 \\6 & {- 12} & 3 & 10 & {- 10} & {- 3} & 12 & {- 6} \\4 & {- 8} & 8 & {- 4} & {- 4} & 8 & {- 8} & 4 \\3 & {- 6} & 10 & {- 12} & 12 & {- 10} & 6 & {- 3}\end{bmatrix} \cdot \frac{1}{8}}}}} & {{Eq}.\mspace{14mu} (15)}\end{matrix}$

X is an 8×8 pixel block, and the products on the right are matrixmultiplies. One method for computing the DCT employs the use of signedpowers of two for computing the multiplication products. In this way nohardware multipliers are needed; but rather, the products are created byshifts and additions thereby reducing the overall logic. In someembodiments, hardware multipliers may be used. In addition, scalingfactors may be used, which may also reduce the required logic. Toretrieve the original pixels (X), the integer matrix is scaled such thatthe inverse DCT implemented by the inverse 2D transform module 314yields the original values of X. One form of the inverse AVC-DCT of theinverse transform module 314 comprises the following:

X=(C ^(T) ·H(X)·C)

S _(i,j),   Eq. (16)

or by substitution:

X=C _(s) ^(T) ·H(X)·C _(s),   Eq. (17)

where C_(s)=C

S_(i,j) and the symbol

denotes element by element multiplication. One example of scalingfactors that may be used is given below:

$\begin{matrix}{S_{i,j} = {\frac{1}{\sum\limits_{i,{j = 0}}^{7}C_{({i,j})}^{2}} = \begin{matrix}0.0020 \\0.0017 \\0.0031 \\0.0017 \\0.0020 \\0.0017 \\0.0031 \\0.0017\end{matrix}}} & {{Eq}.\mspace{14mu} (18)}\end{matrix}$

After the 2D transform, the overlapped block processing module 350changes the spatial dimension from 2D to 1D by zig-zag scanning theoutput of each 2D transform from low frequency to highest frequency, sothe 2D transformed block bs(i,j,f) becomes instead bs(zz_index, f),where 0≦zz_index≦63, 0≦f≦3. The mapping of (i,j) to zz_index is given bythe zig_zag_scan vector below, identical to the scan used in MPEG-2video encoding. In some embodiments, the 2D dimension may be retainedfor further processing. If the first row of the 2D matrix is given byelements 0 through 7, the second row by 8 through 16, thenzig_zag_scan[0:63] specifies a 2D to 1D mapping as follows:

zig_zag_scan[0:63] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5, 12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28, 35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51, 58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };

At a time corresponding to computation of the 2D transform (e.g.,subsequent to the computation), the temporal mode (TemporalMode) isselected by temporal mode module 302 (also referred to herein astemporal mode logic). When utilizing a Haar 1-D transform, theTemporalMode defines whether 2D or 3D thresholding is enabled, whichHaar subbands are thresholded for 3D thresholding, and which spatialsubbands are thresholded for 2D thresholding. The temporal mode mayeither be SPATIAL_ONLY, FWD4, BAK4, or M0DE8, as further describedhereinbelow. The temporal mode is signaled to the 2D threshold module306 and/or the 3D threshold module 310 (herein also collectively orindividually referred to as threshold logic or thresholding logic). Ifthe TemporalMode==SPATIAL ONLY, then the 2D transformed blockbs(zz_index, 1) is thresholded yielding bst(zz_index, 1). If thetemporal mode is not SPATIAL_ONLY, then bst(zz_index,t) is set tobs(zz_index, f).

Following spatial thresholding by the 2D threshold module 306, thebst(zz_index, t) blocks are 1D transformed at the 1D transform module308 (also referred to herein as transform logic), yieldingbhaar(zz_index, f). The 1D transform module 308 takes in samples fromthe 2D transformed 8×8 blocks that have been remapped by zig-zagscanning the 2D blocks to 1D, so that bs(zz_index,f) represents a sampleat 0≦zz_index≦63 and 0≦f≦3 so that the complete set of samples isbs(0:63, 0:3). Whereas the 2D transform module 304 operates on spatialblocks of pixels from the matched frames, the 1D transform module 308operates on the temporal samples across the 2D transformed frame blocksat a given spatial index 0≦zz_index≦63. Therefore, there are sixty-four(64) 1D transforms for each set of four (4) 8×8 blocks.

As indicated above, the 1D Transform used for the VDN system embodiment200 c-1 is a modified three-level, 1D Haar transform, though not limitedto a Haar-based transform or three levels. That is, in some embodiments,other 1D transforms using other levels, wavelet-based or otherwise, maybe used, including DCT, WHT, DWT, etc., with one of the goals comprisingconfiguring the samples into filterable frequency bands. Beforeproceeding with processing of the overlapped block processing module350, and in particular, 1D transformation, attention is re-directed toFIGS. 6A-6D, which illustrates various steps in the modification of a 1DHaar transform in the context of the reduction in frame matchingdescribed in association with FIGS. 2A-2C. It should be understood thateach filter in the evolution of the 1D Haar shown in respective FIGS.6A-6D may be a stand-alone filter that can be used in some VDN systemembodiments. A modified Haar wavelet transform is implemented by the 1Dtransform module 308 (and the inverse in inverse 1D transform module312) for the temporal dimension that enables frame collapsing in themanner described above for the different embodiments. In general, a Haarwavelet transform in one dimension transforms a 2-element vectoraccording to the following equation:

$\begin{matrix}{{\begin{pmatrix}{y(1)} \\{y(2)}\end{pmatrix} = {T \cdot \begin{pmatrix}{x(1)} \\{x(2)}\end{pmatrix}}},{{{where}\mspace{14mu} T} = {\frac{1}{\sqrt{2}}{\begin{pmatrix}1 & 1 \\1 & {- 1}\end{pmatrix}.}}}} & {{Eq}.\mspace{14mu} (19)}\end{matrix}$

From Eq (19), it is observed that the Haar transform is a sum-differencetransform. That is, two elements are transformed by taking their sum anddifference, where the term 1/√{square root over (2)} is energypreserving, or normalization. It is standard in wavelet decomposition toperform a so-called “critically sampled full dyadic decomposition.”

A signal flow diagram 600 a for the Haar transform, including theforward and inverse transforms, is shown in FIG. 6A. The signal flowdiagrams 600 a, 600 b, 600 c, or 600 d in FIGS. 6A-6D are illustrativeof example processing (from top-down) of 2D transform samples that maybe implemented collectively by the 1D transform module 308 and theinverse 1D transform module 312. The signal flow diagram 600 a isdivided into a forward transform 602 and an inverse transform 604. Thenormalizing term 1/√{square root over (2)} may be removed if thetransform is rescaled on inverse (e.g., as shown in FIGS. 6A-6D byfactors of four (4) and two (2) with x2 and x4, respectively, in theinverse transform section 604). In addition, while FIG. 6A shows theinverse Haar transform 604 following directly after the forwardtransform 602, it should be appreciated in the context of the presentdisclosure that in view of the denoising methods described herein,thresholding operations (not shown) may intervene in some embodimentsbetween the forward transform 602 and the inverse transform 604.

In a dyadic wavelet decomposition, a set of samples are “run” throughthe transformation (e.g. as given by Eq. (19)), and the result issubsampled by a factor of two (2), known in wavelet theory as being“critically sampled.” Using 8-samples (e.g., 2D transformed, co-locatedsamples) as an example in FIG. 6A, the samples b0 through b7 aretransformed in a first stage 606 by Eq. (19) pair-wise on [b0 b1], [b2b3], . . . [b6 . . . b7], producing the four (4), low frequency,sum-subband samples [L00, L01, L02, L03] and four (4), high frequency,difference-subband samples [H00, H01, H02, H03]. Since half the subbandsare low-frequency, and half are high-frequency, the result is what isreferred to as a dyadic decomposition.

An 8-sample full decomposition continues by taking the four (4),low-frequency subband samples [L00, L01, L02, L03] and running themthrough a second stage 608 of the Eq. (19) transformation with criticalsampling, producing two (2) lower-frequency subbands [L10, L11] and two(2) higher frequency subbands [H10, H11]. A third stage 610 on the two(2) lowest frequency samples completes the full dyadic decomposition,and produces [L20, H20].

The 1D Haar transform in FIG. 6A enables a forward transformation 602and inverse transformation 604 when all samples (e.g., bd(i,j,0:7)) areretained on output. This is the case for a 3D accumulation buffer.However, as discussed hereinabove, the output of the 2D+1 accumulationbuffer (see FIGS. 2A-2B) requires only bd(i,j,4) and bd(i,j,7).Therefore, simplification of the flow diagram of 600 a, where only thebd(i,j,4) and bd(i,j,7) are retained, results in the flow diagramdenoted as 600 b and illustrated in FIG. 6B.

In FIG. 6B, since bd(i,j,4) and bd(i,j,7) are the desired outcome, theentire left-hand side of the transformation process requires only thesummation of the first four (4) samples b(i,j,0:3). As described below,this summation may occur outside the 1D transform (e.g., as part of theframe matching process). On the right side, there has been a re-orderingof the samples when compared to the flow diagram of FIG. 6A (i.e., ┌b4b5 b6 b7┐ to ┌b4 b7 b6 b5┐). In addition, only the sum of b(i,j,5) andb(i,j6) is required (not a subtraction). Accordingly, by using thissimplified transform in flow diagram 600 b, with sample reordering, andframe-matching the sum of the first four (4) frames to the referenceframe F4(t), the first four (4) frames may be collapsed into a singleframe. For frames 5 and 6, an assumption is made that both frames havebeen frame matched to the reference producing M5(t) and M6(t), and hencethose frames may be summed prior to (e.g., outside) the 1D transform.

When the summing happens outside the 1D transform, the resulting 1Dtransform is further simplified as shown in the flow diagram 600 c ofFIG. 6C, where b0+b1+b2+b3 represents the blocks from the frame-matchedsum of frames F0(t) through F3(t), that is MSUM0123(t), prior to the 1Dtransform, and b5+b6 represents the sum of blocks b(i,j,5) and b(i,j,6)from frame-matched and summed frames M5(t) and M6(t), that is MSUM56(t),the summation implemented prior to the 1D transform. Note that for 2D+1accumulation embodiments corresponding to Haar modificationscorresponding to FIGS. 6B and 6C, there is a re-ordering of samples suchthat b7 is swapped in and b5 and b6 are moved over.

Referring to FIG. 6D, shown is a flow diagram 600 d that is furthersimplified based on a 2D accumulation buffer, as shown in FIG. 2C. Thatis, only one input sample, bd4, has been retained on output to the 2Daccumulation buffer. Note that in contrast to the 2D+1 accumulationbuffer embodiments where sample re-ordering is implemented as explainedabove, the Haar modifications corresponding to FIG. 6D involve no samplere-ordering.

Continuing now with the description pertaining to 1D transformation inthe overlapped block processing module 350, and in reference to FIG. 3,at each zz_index, the 1D transform 308 processes the four (4) samplesbs(zz_index, 0:3) to produce bhaar(0:63,0:3), which includes Haarsubbands [L20, H20, H02, H11]. The first index is from the stages 0, 1,2, so L20 and H20 are from the 3^(rd) stage 610 (FIG. 6D), H02 is fromthe first stage 606 (FIG. 6D), and H11 is from the 2^(nd) stage (608).These Haar subbands are as illustrated in FIG. 6D and are furtherinterpreted with respect to the matched frames as follows:

-   L20: summation of all matched blocks b(i,j,0:7);-   H20: difference between matched blocks in MSUM0123(t) and the total    sum of matched blocks in M4(t), M5(t) and 2×MSUM67(t);-   H02: For 2D+1 Accumulation Buffer (FIG. 6C), difference between    matched blocks b4 and b7 from frames M4(t), and M7(t), respectively.    For 2D Accumulation Buffer (FIG. 6D), difference between matched    blocks b4 and b5 from frames M4(t), and M5(t), respectively.-   H11: For 2D+1 Accumulation Buffer (FIG. 6C), difference between sum    (b4+b7) matched blocks from M4(t) and M7(t) respectively, and    2×MSUM56(t). For 2D Accumulation Buffer (FIG. 6D), difference    between sum (b4+b5) matched blocks from M4(t) and M5(t)    respectively, and 2×MSUM56(t).

With reference to FIG. 7, shown is a schematic diagram 700 thatconceptually illustrates the various temporal mode selections thatdetermine whether 3D or 2D thresholding is enabled, and whether Haar orspatial subbands are thresholded for 3D and 2D, respectively. As shown,the selections include M0DE8, BAK4, FWD4, and SPATIAL, explained furtherbelow. The temporal mode selections make it possible for the VDN systems200 to adapt to video scenes that are not temporally correlated, such asscene changes, or other discontinuities (e.g., a viewer blinks his orher eye or turns around during a scene that results in the perception ofa discontinuity, or discontinuities associated with a pan shot, etc.) byenabling a determination of which frames of a given temporal sequence(e.g., F0(t)-F7(t)) can be removed from further transform and/orthreshold processing. The TemporalMode selections are as follows:

-   For TemporalMode==M0DE8 or TemporalMode==SPATIAL: (no change). In    other words, for TemporalMode set to M0DE8 or SPATIAL, there is no    need to preprocess the samples before the 1D transform. For FWD4 or    BAK4 temporal modes, the samples undergo preprocessing as specified    below.

For TemporalMode == FWD4: (Input sample b0+b1+b2+b3 from MSUM0123(t) isset equal to zero); For TemporalMode == BAK4: (Input sample b4 set equalto 4 * b4);

In one embodiment, a temporal mode module 302 computes TemporalModeafter the 2D transform is taken on the 8×8×4 set of blocks. TheTemporalMode takes on one of the following four values:

(a) SPATIAL: when the TemporalMode is set to SPATIAL, 2D (spatial)thresholding takes place after the 2D transform on each 2D blockbs(0:63, t) 0≦t≦3 separately to produce bst(0:3, t). In other words,under a spatial temporal mode, there is no 1D transformation orthresholding of 3D blocks (temporal dimension is removed for thisiteration). If the Temporal Mode is not set to SPATIAL, then bst(0:63,t) is set to bs(0:63, t) (pass-through).

(b) FWD4: when the TemporalMode is set to FWD4, right-sided (later)samples from M4(t), M5(t) and MSUM67(t) are effectively used, andsamples from the left (earlier) side, in MSUM0123(t), are not used.

(c) BAK4: when the TemporalMode is set to BAK4, left-sided (earlier)samples from MSUM0123(t) and M4(t) are effectively used, and samplesfrom the right (later) side, in M5(t) and MSUM67(t), are not used.

(d) M0DE8: when the TemporalMode is set to M0DE8, all samples are used.

The TemporalMode is computed for every overlapped set of blocks (e.g.,its value is computed at every overlapped block position). Therefore,implicitly, TemporalMode is a function of i,j, so TemporalMode(i,j)denotes the value of TemporalMode for the i,j-th pixel position in theoverlapped block processing. The shorthand, “TemporalMode” is usedthroughout herein with the understanding that TemporalMode comprises avalue that is computed at every overlapped block position of a givenframe to ensure, among other reasons, proper block matching wasachieved. In effect, the selected temporal mode defines the processing(e.g., thresholding) of a different number of subbands (e.g., L20, H20,etc.).

Having described the various temporal modes implemented in the VDNsystems 200, attention is now directed to a determination of whichtemporal mode to implement. To determine TemporalMode, the SubbandSAD iscomputed (e.g., by the 2D transform module 304 and communicated to thetemporal mode module 302) between the blocks bs(0:63, k) and bs(0:63, 1)for 0≦k≦3 (zero for k=1), which establishes the closeness of the matchof co-located blocks of the inputted samples (from the matched frames),using the low-frequency structure of the blocks where the signal canmostly be expected to exceed noise. Explaining further, thedetermination of closeness of a given match may be obscured or skewedwhen the comparison involves noisy blocks. By rejecting noise, the levelof fidelity of the comparison may be improved. In one embodiment, theVDN system 200 c-1 effectively performs a power compaction (e.g., aforward transform, such as via DCT) of the blocks at issue, whereby mostof the energy of a natural video scene are power compacted into a few,more significant coefficients (whereas noise is generally uniformlydistributed in a scene). Then, a SAD is performed in the DCT domainbetween the significant few coefficients of the blocks under comparison(e.g., in a subband of the DCT, based on a predefined threshold subbandSAD value, not of the entire 8×8 block), resulting in removal of asignificant portion of the noise from the computation and henceproviding a more accurate determination of matching.

Explaining further, in one embodiment, the subbandSAD is computed usingthe ten (10) lowest frequency elements of the 2D transformed blocksbs(0:9, 0:3) where the frequency order low-to-high follows the zig-zagscanning specified hereinbefore. In some embodiments, fewer or greaternumbers of lowest frequency elements may be used. Accordingly, for thisexample embodiment, the SubbandSAD(k) is given by the followingequation:

$\begin{matrix}{{{{SubbandSAD}(k)} = {\sum\limits_{z = 0}^{9}{{{{bs}\left( {z,k} \right)} - {{bs}\left( {z,1} \right)}}}}},{{{where}\mspace{14mu} 0} \leq k \leq 3},{{{and}\mspace{14mu} {{SubbandSAD}(1)}} = 0.}} & {{Eq}.\mspace{14mu} (20)}\end{matrix}$

Integer counts SubbandFWDCount4, SubbandBAKCount4 and SubbandCount8 maybe defined as follows:

$\begin{matrix}{{{SubbandCountFWD}\mspace{11mu} 4} = {\sum\limits_{k = 1}^{3}{{SetToOneOrZero}\left( {{{SubbandSAD}(k)} < {Tsubbandsad}} \right)}}} & {{Eq}.\mspace{14mu} \left( {21a} \right)} \\{{{SubbandCountBAK}\mspace{11mu} 4} = {\sum\limits_{k = 0}^{1}{{SetToOneOrZero}\left( {{{SubbandSAD}(k)} < {Tsubbandsad}} \right)}}} & {{Eq}.\mspace{14mu} \left( {21b} \right)} \\{{{SubbandCount}\mspace{11mu} 8} = {\sum\limits_{k = 0}^{3}{{SetToOneOrZero}\left( {{{SubbandSAD}(k)} < {Tsubbandsad}} \right)}}} & {{Eq}.\mspace{14mu} \left( {21c} \right)}\end{matrix}$

where the function SetToOneOrZero(x) equals 1 when it's argumentevaluates to TRUE, and zero otherwise, and Tsubbandsad is a parameter.In effect, Eqns. 21a-21c are computed to determine how many of theblocks are under the subband SAD threshold, and hence determine thetemporal mode to be implemented. For instance, referring to Eq. 21c, forM0DE8, the DCT of b0+b1+b2 +b3 should be close enough in the lowerfrequencies to the DCT of b4 (and likewise, the DCT of b5 and b6+7should be close enough in the lower frequencies to b4). Note that k=0 tok=3 since there is b0+b1+b2+b3, b4 (though k=1 is meaningless since thesubband SAD of b4 with itself is zero), b5, and b6+7.

In Eq. 21 a, for FWD4, the closeness of b4 with b5 and b6+7 isevaluated, so the numbering for k goes from 1-3 (though should go from 2to 3 since 1 is meaningless as explained above). In Eq. 21 b, numberingfor k goes from 0 to 1, since only the zeroth sample is checked (i.e.,the b0+1+2+3 sample, and again, 1 is meaningless since b4 always matchesitself).

Accordingly, using SubbandCountFWD4, SubbandCountBAK4, andSubbandCount8, TemporalMode is set as follows:

If SubbandCount8 == 4, then TemporalMode = MODE8; else ifSubbandCountFWD4 == 3 then TemporalMode = FWD4; else if SubbandCountBAK4== 2 then TemporalMode = BAK4; else TemporalMode = SPATIAL.Note that this scheme favors FWD4 over BAK4, but if M0DE8 is notsignaled, then only one of FWD4 or BAK4 can be satisfied anyway.

Thresholding is performed on the 2D or 3D transformed blocks during theoverlapped block processing. For instance, when TemporalMode is set toSPATIAL, the 2D threshold module 306 is signaled, enabling the 2Dthreshold module 306 to perform 2D thresholding of the 2D transformedblock bs(0:63, 1) from F4(t). According to this mode, no thresholdingtakes place on the three (3) blocks from MSUM0123(t), M5(t) or MSUM67(t)(i.e., there is no 2D thresholding on bs(0:63, 0), bs(0:63, 2), bs(0:63,3)).

Reference is made to FIG. 8, which is a schematic diagram 800 thatillustrates one example embodiment of time-space frequency partitioningby thresholding vector, T_(—)2D, and spatial index matrix, S_(—)2D, eachof which can be defined as follows:

-   T_(—)2D(0:3): 4-element vector of thresholds-   S_(—)2D(0:3,2): 4×2 element matrix of spatial indices    Together, T_(—)2D and S_(—)2D define the parameters used for    thresholding the 2D blocks bs(0:63, 1). For only the 8×8 2D    transformed block bs(0:63, 1), the thresholded block bst(0:63, 1)    may be derived according to Eq. (22) below:

$\begin{matrix}{{{bst}\left( {z,t} \right)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} {{{bs}\left( {z,1} \right)}}} < {{T\_}2{D(j)}\mspace{14mu} {and}\mspace{14mu} {S\_}2{D\left( {j,0} \right)}} \leq z \leq {{S\_}2{D\left( {j,1} \right)}}} \\{{bs}\left( {z,1} \right)} & {otherwise}\end{matrix} \right.} & {{Eq}.\mspace{14mu} (22)}\end{matrix}$

In Eq. (22), T_(—)2D(j) defines the threshold used for block 1,bs(0:63, 1) from M4(t), over a span of spatial indices S_(—)2D(j,0) toS_(—)2D j,1) for j=0 . . . 3. Equivalently stated, elements ofbs(S_(—)2D(j,0): S_(—)2D(j,1),1) are thresholded by comparing the valuesof those elements to the threshold T_(—)2D(j), and the values inbs(S_(—)2D(j,0): S_(—)2D(j,1),1) are set to zero when their absolutevalues are less than T_(—)2D(j). Note that none of the matched framesMSUM0123(t), MSUM67(t) or M5(t) undergo 2D thresholding, only blocksfrom M4(t), bs(0:63, 1).

The spatial index matrix S_(—)2D together with T_(—)2D define a subsetof coefficients in the zig-zag scanned spatial frequencies asillustrated in FIG. 8. The 2D space has been reduced to 1D by zig zagscanning.

Thresholding (at 3D threshold module 310) of the 3D transformed blocksbhaar(0:63,0:3) output from 1D transform module 308 is performed whenTemporalMode is set to either FWD4, BAK4 or M0DE8. Otherwise, whenTemporalMode is set to SPATIAL, the output of 3D thresholding module 310(e.g., bhaart(0:63,0:3)) is set to the input of the 3D thresholdingmodule 310 (e.g., input equals bhaar(0:63,0:3)) without modification.The 3D thresholding module 310 uses threshold vectors T_(—)3D(j) andspatial index matrix S_(—)3D(j,0:1) defined hereinabove in 2Dthresholding 306, except using eight (8) thresholds so 0≦j≦8.

An additional threshold vector TSUB(j,0:1) is needed for 3D thresholding310, which defines the range of temporal subbands for each j. Forexample, TSUB(0,0)=0 with TSUB(0,1)=1 along with T_(—)3D(0)=100 andS_(—)3D(0,0)=0 and S_(—)3D(0,1)=32 indicates that for j=0, 3Dthresholding with the threshold of 100 is used across Haar subbands L20and H20 and spatial frequencies 0 to 32.

Thresholding 310 of the 3D blocks is followed identically to the 2Dthresholding 306 case following Eq. (22), but substituting T 3D andS_(—)3D for T_(—)2D and S_(—)2D. For 3D thresholding 310, unlike 2D, allfour (4) blocks bhaar(0:63,0:3) are thresholded.

For MODE8, all Haar subbands are thresholded. For FWD4 and BAK4, only asubset of the Haar subbands is thresholded. The following specifieswhich subbands are thresholded according to TemporalMode:

If TemporalMode == MODE8 threshold [L20, H20, H02, H11 ] Haar SubbandsIf TemporalMode == FWD4 threshold [H20, H02, H11 ] Haar Subbands IfTemporalMode == BAK4 threshold [L20, H20] Haar Subbands

Having described example embodiments of thresholding in VDN systems,attention is again directed to FIG. 3 and inverse transform and outputprocessing. Specifically, inverse transforms include the inverse 1Dtransform 312 (e.g., Haar) followed by the inverse 2D transform (e.g.,AVC integer DCT), both described hereinabove. It should be understood byone having ordinary skill in the art that other types of transforms maybe used for both dimension, or a mix of different types of transformsdifferent than the Haar/AVC integer combination described herein in someembodiments. For a 1D Haar transform, the inverse transform proceeds asshown in FIG. 6D and explained above.

The final denoised frame of FD4(t) using the merged accumulation buffersis specified in Eq. (23) as follows:

$\begin{matrix}{{{FD}\; 4\left( {i,j} \right)} = \frac{A\left( {i,j} \right)}{W\left( {i,j} \right)}} & {{Eq}.\mspace{14mu} (23)}\end{matrix}$

For uniform weighting where w(i,j)=1 for all overlapped blocks, Eq. (23)amounts to a simple divide by 16 (for step size=2). However, ifselectively omitting blocks, then Eq. (23) amounts to division by anumber 1≦W(i,j)≦16. After the 2D inverse transform 314 follows the 1Dinverse transform 312, the block bd(i,j,1) represents the denoised blockof F4(t). This denoised block is accumulated (added into) in the A4(t)accumulation buffer(s) 360 (e.g., accumulates the denoised estimates viarepeated loops back to the 2D transform module 304 to index or shift inpixels to repeat the processing 350 for a given reference frame), andthen output via normalization block 370.

Having described various embodiments of VDN systems 200, it should beappreciated that one method embodiment 900, implemented in oneembodiment by the logic of the VDN system 200 c-1 (FIG. 3) and shown inFIG. 9, comprises for a first temporal sequence of first plural frames,frame matching the first plural frames, at least a portion of the firstplural frames corrupted with noise (902), at a time corresponding tocompletion of all frame matching for the first temporal sequence,overlap block processing plural sets of matched blocks among the firstplural matched frames (904).

Another method embodiment 1000 a, shown in FIG. 10A and implemented inone embodiment by frame alignment module 310 a (FIG. 4A), comprisesreceiving a first temporal sequence of first plural frames and areference frame, at least a portion of the first plural frames and thereference frame corrupted by noise (1002), frame matching the firstplural frames (1004), summing the frame-matched first plural frames withat least one of the first plural frames to provide a first summationframe (1006), delaying the frame-matched first plural frames to providea first delayed frame (1008), frame matching the first summation frameto the reference frame to provide a second summation frame (1010),self-combining the second summation frame multiple times to provide amultiplied frame (1012), summing the multiplied frame with the firstdelayed frame and the reference frame to provide a third summation frame(1014), delaying the third summation frame to provide a delayed thirdsummation frame (1016), frame matching the delayed third summation framewith the reference frame to provide a fourth summation frame (1018), andoutputting the first delayed frame, the second summation frame, thereference frame, and the fourth summation frame for further processing(1020).

Another method embodiment 1000 b, shown in FIG. 10B and implemented inone embodiment by frame alignment module 310 b (FIG. 4B), comprisesreceiving a first temporal sequence of first plural frames and areference frame, at least a portion of the first plural frames and thereference frame corrupted by noise (1022); frame matching the firstplural frames (1024); summing the frame-matched first plural frames withat least one of the first plural frames to provide a first summationframe (1026); delaying the frame-matched first plural frames to providea first delayed frame (1028); delaying the first summation frame toprovide a delayed first summation frame (1030); frame matching thedelayed first summation frame to the reference frame to provide a secondsummation frame (1032); self-combining the second summation framemultiple times to provide a multiplied frame (1034); summing themultiplied frame with the first delayed frame and the reference frame toprovide a third summation frame (1036); delaying the third summationframe to provide delayed third summation frame (1038); frame matchingthe delayed third summation frame with the reference frame to provide afourth summation frame (1040); and outputting the first delayed frame,the second summation frame, the reference frame, and the fourthsummation frame for further processing (1042).

Another method embodiment 1100, shown in FIG. 11 and implemented in oneembodiment by VDN system 200 corresponding to FIGS. 2A-2C and FIG. 3,comprises receiving a first temporal sequence of video frames, the firsttemporal sequence corrupted with noise (1102); frame matching the videoframes according to a first stage of processing (1104); denoising thematched frames according to a second stage of processing, the secondstage of processing commencing responsive to completion of the firststage of processing for all of the video frames, the second stage ofprocessing comprising overlapped block processing (1106); and whereindenoising further comprises accumulating denoised pixels for eachiteration of the overlapped block processing in a 2D+c accumulationbuffer, the 2D accumulation buffer corresponding to the denoised pixelscorresponding to a reference frame of the video frames, where ccomprises an integer number of non-reference frame buffers greater thanor equal to zero (1108).

Another method embodiment 1200 a, shown in FIG. 12A and implemented inone embodiment by the overlapped block processing module 350 (FIG. 3),comprises forward transforming a set of co-located blocks correspondingto plural matched frames (1202); computing a difference measure for asubset of coefficients between a set of transformed blocks and areference block, the computation in the 2D transform domain (1204); andselectively thresholding one or more of the co-located transformedblocks based on the number of transformed blocks having a differencemeasure below a predetermined threshold (1206).

Another method embodiment 1200 b, shown in FIG. 12B and implemented inone embodiment by the overlapped block processing module 350 (FIG. 3),comprises computing a subband difference measure for a set ofcoefficients of a 2D transformed set of co-located matched blockscorresponding to plural matched frames blocks, the computationimplemented in a 2D transform domain (1208); and selectivelythresholding one or more of the co-located 2D or 3D transformed matchedblocks based on the computed subband difference measure in comparison toa defined subband difference measure threshold value (1210).

Another method embodiment 1300, shown in FIG. 13 and implemented in oneembodiment by the overlapped block processing module 350 (FIG. 3),comprises receiving matched frames (1302); forward transformingco-located blocks of the matched frames (1304); and thresholding thetransformed co-located blocks corresponding to a subset of the matchedframes in at least one iteration (1306).

Another method embodiment 1400, shown in FIG. 14 and implemented in oneembodiment by the frame matching module 402 (FIG. 5) comprises receivingplural frames of a video sequence, the plural frames corrupted withnoise (1402); filtering out the noise from the plural frames (1404);block matching the filtered frames to derive a first set of motionvectors (1406); scaling the first set of motion vectors (1408); derivinga single scaled motion vector from the scaled motion vectors (1410); andblock matching the plural frames based on the scaled motion vector toderive a refined motion vector (1410).

Any process descriptions or blocks in flow charts or flow diagramsshould be understood as representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process, and alternateimplementations are included within the scope of the present disclosurein which functions may be executed out of order from that shown ordiscussed, including substantially concurrently or in reverse order,depending on the functionality involved, as would be understood by thosereasonably skilled in the art. In some embodiments, steps of a processidentified in FIGS. 9-14 using separate boxes can be combined. Further,the various steps in the flow diagrams illustrated in conjunction withthe present disclosure are not limited to the architectures describedabove in association with the description for the flow diagram (asimplemented in or by a particular module or logic) nor are the stepslimited to the example embodiments described in the specification andassociated with the figures of the present disclosure. In someembodiments, one or more steps may be added to one or more of themethods described in FIGS. 9-14, either in the beginning, end, and/or asintervening steps.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations,merely set forth for a clear understanding of the principles of the VDNsystems and methods. Many variations and modifications may be made tothe above-described embodiment(s) without departing substantially fromthe spirit and principles of the disclosure. Although all suchmodifications and variations are intended to be included herein withinthe scope of this disclosure and protected by the following claims, thefollowing claims are not necessarily limited to the particularembodiments set out in the description.

1. A method, comprising: receiving a first temporal sequence of firstplural frames and a reference frame, at least a portion of the firstplural frames and the reference frame corrupted by noise; frame matchingthe first plural frames; summing the frame-matched first plural frameswith at least one of the first plural frames to provide a firstsummation frame; delaying the frame-matched first plural frames toprovide a first delayed frame; frame matching the first summation frameto the reference frame to provide a second summation frame;self-combining the second summation frame multiple times to provide amultiplied frame; summing the multiplied frame with the first delayedframe and the reference frame to provide a third summation frame;delaying the third summation frame to provide a delayed third summationframe; frame matching the delayed third summation frame with thereference frame to provide a fourth summation frame; and outputting thefirst delayed frame, the second summation frame, the reference frame,and the fourth summation frame for further processing.
 2. The method ofclaim 1, wherein the outputted frames comprise an approximation of areceived raw video sequence, the frames of the received raw videosequence greater in quantity than the outputted frames.
 3. The method ofclaim 1, wherein each of the delaying comprises an imposition of adefined delay that varies based on the inputted frame.
 4. The method ofclaim 1, wherein outputting comprises outputting to a three-dimensional(3D) denoising loop, the 3D denoising loop subjecting the outputtedframes to overlapped block processing.
 5. The method of claim 1, whereineach of the frame matching comprises processing of luminance andchrominance data of the inputted frames.
 6. The method of claim 5,wherein the processing comprises motion estimation and motioncompensation.
 7. The method of claim 1, wherein each of the outputs ofthe frame matching comprises a set of motion vectors representing ablock of pixels in one of the inputted frames, the block of pixelsapproximating a block of pixels in the other of the inputted frames. 8.The method of claim 1, wherein the frame-matched first plural framescomprises an estimate of the at least one of the first plural framesfrom another of the first plural frames.
 9. The method of claim 1,wherein the second summation frame comprises an estimate of thereference frame from the first plural frames.
 10. The method of claim 1,wherein the fourth summation frame comprises an estimate of thereference frame from the third summation frame delayed in time.
 11. Amethod, comprising: receiving a first temporal sequence of first pluralframes and a reference frame, at least a portion of the first pluralframes and the reference frame corrupted by noise; frame matching thefirst plural frames; summing the frame-matched first plural frames withat least one of the first plural frames to provide a first summationframe; delaying the frame-matched first plural frames to provide a firstdelayed frame; delaying the first summation frame to provide a delayedfirst summation frame; frame matching the delayed first summation frameto the reference frame to provide a second summation frame;self-combining the second summation frame multiple times to provide amultiplied frame; summing the multiplied frame with the first delayedframe and the reference frame to provide a third summation frame;delaying the third summation frame to provide delayed third summationframe; frame matching the delayed third summation frame with thereference frame to provide a fourth summation frame; and outputting thefirst delayed frame, the second summation frame, the reference frame,and the fourth summation frame for further processing.
 12. The method ofclaim 11, wherein the outputted frames comprise an approximation of areceived raw video sequence, the frames of the received raw videosequence greater in quantity than the outputted frames.
 13. The methodof claim 11, wherein each of the delaying comprises an imposition of adefined delay that varies based on the inputted frame.
 14. The method ofclaim 11, wherein outputting comprises outputting to a three-dimensional(3D) denoising loop, the 3D denoising loop subjecting the outputtedframes to overlapped block processing.
 15. The method of claim 11,wherein each of the frame matching comprises motion estimation andmotion compensation of luminance and chrominance data of the inputtedframes, and wherein each of the outputs of the frame matching comprisesa set of motion vectors representing a block of pixels in one of theinputted frames, the block of pixels approximating a block of pixels inthe other of the inputted frames.
 16. The method of claim 11, whereinthe frame-matched first plural frames comprises an estimate of the atleast one of the first plural frames from another of the first pluralframes.
 17. The method of claim 11, wherein the second summation framecomprises an estimate of the reference frame from the first pluralframes.
 18. The method of claim 11, wherein the fourth summation framecomprises an estimate of the reference frame from the third summationframe delayed in time.
 19. A system, comprising: an overlapped blockprocessing module configured to provide three-dimensional (3D) denoisingof plural frames corresponding to a raw video sequence; and a framealignment module configured to represent the raw video sequence withmotion compensated frames corresponding to the raw video sequence, themotion compensated frames consisting of the plural frames and fewer inquantity than the quantity of frames of the raw video sequence, theplural frames based on prior temporally matched frames corresponding tothe raw video sequence.
 20. The system of claim 19, wherein theoverlapped block processing module and the frame alignment module areconfigured in programmable hardware.